In certain sensitive locations where security is a concern (e.g., airports, train stations, military bases), people and objects are often closely monitored to detect suspicious (e.g., potentially dangerous and/or malicious) activities such as loitering, the breach of secure perimeters, the leaving of objects (e.g., unattended bags, stopped vehicles, etc.) and other activities that might indicate a security threat.
Typically, object tracking applications for monitoring such activities operate as single-track solutions for each monitored person or object, and decisions regarding activities (e.g., loitering, perimeter breach, left objects, etc.) are made on that single track. Such approaches are prone to errors, however, due to confusion caused, for example, by occlusions and the merging of multiple objects. These errors often result in false alarms being generated, e.g., where innocent activities or movement are mistaken for suspicious activities. Thus, a significant amount of time and resources may be wasted on relatively trivial occurrences and panic may be unnecessarily generated. Alternatively, methods that operate on a reduced sensitivity in order to compensate for this tendency to generate false alarms often tend to overlook real security threats, which can also have disastrous consequences.
Therefore, there is a need in the art for a method and apparatus for detecting suspicious activities that is capable of reliably detecting such activities with a low false alarm rate.